• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.664-682
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• 2012

To improve the accuracy and enhance the applicability of existing models, this paper proposes a generalized Markov chain model for IEEE 802.11 Distributed Coordination Function (DCF) under the widely adopted assumption of ideal transmission channel. The IEEE 802.11 DCF is modeled by a two dimensional Markov chain, which takes into account unsaturated traffic, backoff freezing, retry limits, the difference between maximum retransmission count and maximum backoff exponent, and limited buffer size based on the M/G/1/K queuing model. We show that existing models can be treated as special cases of the proposed generalized model. Furthermore, simulation results validate the accuracy of the proposed model.

• Journal of applied mathematics & informatics
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• v.37 no.5_6
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• pp.469-480
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• 2019

A generalized tiling is defined as a generalization of the properties of tiling a region of ${\mathbb{Z}}^2$ with dominoes, and comprises tiling with rhombus and any other tilings that admits height functions which can be ordered into a distributive lattice. By using properties of the distributive lattice, we prove that the Markov chain consisting of moving from one height function to the next by a flip is fast mixing and the mixing time ${\tau}({\epsilon})$ is given by ${\tau}({\epsilon}){\leq}(kmn)^3(mn\;{\ln}\;k+{\ln}\;{\epsilon}^{-1})$, where mn is the area of the grid ${\Gamma}$ that is a k-regular polycell. This result generalizes the result of the authors (T-tetromino tiling Markov chain is fast mixing, Theor. Comp. Sci. (2018)) and improves on the mixing time obtained by using coupling arguments by N. Destainville and by M. Luby, D. Randall, A. Sinclair.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.521-523
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• 2021

관광 분야에서 온라인 리뷰의 중요성이 커지고 있다. 온라인 리뷰의 텍스트 데이터는 파악이 어렵다. 이에 본 연구에서는 특정 관광지에 대한 온라인 리뷰 텍스트 데이터가 나타내는 전반적인 의견을 직관적으로 도출하는 방법에 대해 알아보고자, 토픽 모델링과 Markov Chain을 시행했다. '해운대'에 대한 온라인 리뷰를 수집한 후, LDA와 BTM을 활용하여 주제를 도출하고, Markov Chain을 시각화하여 키워드 간의 관계와 전체적인 평가 내용을 확인했다. 사용된 기법은 각자 특징적인 결과를 제시했기 때문에 다양한 기법을 상보적으로 이용하기를 제안하였다.

• Asian Journal of Atmospheric Environment
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• v.10 no.2
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• pp.67-79
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• 2016

The forecasting of air pollution is an important and popular topic in environmental engineering. Due to health impacts caused by unacceptable particulate matter (PM) levels, it has become one of the greatest concerns in metropolitan cities like Karaj City in Iran. In this study, the concentration of $PM_{2.5}$ was predicted by applying a multilayer percepteron (MLP) neural network, a radial basis function (RBF) neural network and a Markov chain model. Two months of hourly data including temperature, NO, $NO_2$, $NO_x$, CO, $SO_2$ and $PM_{10}$ were used as inputs to the artificial neural networks. From 1,488 data, 1,300 of data was used to train the models and the rest of the data were applied to test the models. The results of using artificial neural networks indicated that the models performed well in predicting $PM_{2.5}$ concentrations. The application of a Markov chain described the probable occurrences of unhealthy hours. The MLP neural network with two hidden layers including 19 neurons in the first layer and 16 neurons in the second layer provided the best results. The coefficient of determination ($R^2$), Index of Agreement (IA) and Efficiency (E) between the observed and the predicted data using an MLP neural network were 0.92, 0.93 and 0.981, respectively. In the MLP neural network, the MBE was 0.0546 which indicates the adequacy of the model. In the RBF neural network, increasing the number of neurons to 1,488 caused the RMSE to decline from 7.88 to 0.00 and caused $R^2$ to reach 0.93. In the Markov chain model the absolute error was 0.014 which indicated an acceptable accuracy and precision. We concluded the probability of occurrence state duration and transition of $PM_{2.5}$ pollution is predictable using a Markov chain method.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.252-265
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• 2022

Subsurface velocity is critical for the accurate resolution geological structures. The estimation of acoustic impedance is also critical, as it provides key information regarding the reservoir properties. Therefore, researchers have developed various inversion approaches for the estimation of reservoir properties. The Markov chain Monte Carlo method, which is a stochastic method, has advantages over the deterministic method, as the stochastic method enables us to attenuate the local minima problem and quantify the uncertainty of inversion results. Therefore, the Markov chain Monte Carlo inversion method has been applied to various kinds of geophysical inversion problems. However, studies on the Markov chain Monte Carlo inversion are still very few compared with deterministic approaches. In this study, we reviewed various types of reversible jump Markov chain Monte Carlo applications and explained the key concept of each application. Furthermore, we discussed future applications of the stochastic method.

• Proceedings of the IEEK Conference
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• summer
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• pp.259-262
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• 2004

In most of the character recognition systems, the method of template matching or statistical method using hidden Markov model is used to extract and recognize feature shapes. In this paper, we used modified chain-code which has 8-directions but 4-codes, and made the chain-code of hand-written character, after that, converted it into transition chain-code by applying to HMM(Hidden Markov Model). The transition chain code by HMM is analyzed as signal flow graph by Mason's theory which is generally used to calculate forward gain at automatic control system. If the specific forward gain and feedback gain is properly set, the forward gain of transition chain-code using Mason's theory can be distinguished depending on each object for recognition. This data of the gain is reorganized as tree structure, hence making it possible to distinguish different hand-written characters. With this method, $91\%$ recognition rate was acquired.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.11
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• pp.1131-1135
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• 2014

This paper proposes the enhanced faulty node detection scheme with hybrid algorithm using Markov-chain model on BCH (Bose-Chaudhuri-Hocquenghem) code in naval distributed control networked systems. The probabilistic model-driven approach, on Markov-chain model, in this paper uses the faulty weighting interval factors, which are based on the BCH code. In this scheme, the master node examines each slave-nodes continuously using three defined states : Good, Warning, Bad-state. These states change using the probabilistic calculation method. This method can improve the performance of detecting the faulty state node more efficiently. Simulation results show that the proposed method can improve the accuracy in faulty node detection scheme for real-time naval distributed control networked systems.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.29-42
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• 2018

We combine the integer-valued GARCH(1, 1) model with a generalized regime-switching model to propose a dynamic count time series model. Our model adopts Markov-chains with time-varying dependent transition probabilities to model dynamic count time series called the generalized regime-switching integer-valued GARCH(1, 1) (GRS-INGARCH(1, 1)) models. We derive a recursive formula of the conditional probability of the regime in the Markov-chain given the past information, in terms of transition probabilities of the Markov-chain and the Poisson parameters of the INGARCH(1, 1) process. In addition, we also study the forecasting of the Poisson parameter as well as the cumulative impulse response function of the model, which is a measure for the persistence of volatility. A Monte-Carlo simulation is conducted to see the performances of volatility forecasting and behaviors of cumulative impulse response coefficients as well as conditional maximum likelihood estimation; consequently, a real data application is given.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.317-320
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• 2022

This paper studies real-time error in the context of monitoring a symmetric binary information source over a delay system. To obtain the average real-time error, the delay system is modeled and analyzed as a discrete time Markov chain with a finite state space. Numerical analysis is performed on various system parameters such as state transition probabilities of information source, transmission times, and transmission frequencies. Given state transition probabilities and transmission times, we investigate the relationship between the transmission frequency and the average real-time error. The results can be used to investigate the relationship between real-time errors and age of information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2253-2258
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• 2008

To design micro flow sensor varying depending on temperature of driving heater in the detector of Oxide semiconductor, Markov chain MCM(MCMCM), which is a kind of stochastic and microscopic method, was introduced. The formulation for the thermal transfer equation based on the FDM to obtain the MCMCM solution was performed and investigated, in steady state case. MCMCM simulation was successfully applied, so that its application can be expanded to a three-dimensional model with inhomogeneous material and complicated boundary.